Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions

Collin Annabelle (), Hejblum Boris P. (), Vignals Carole (), Lehot Laurent (), Thiébaut Rodolphe (), Moireau Philippe () and Prague Mélanie ()
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Collin Annabelle: Inria, Inria Bordeaux – Sud-Ouest, Bordeaux INP, IMB UMR 5251, Université Bordeaux, Talence, France
Hejblum Boris P.: Inria, Inria Bordeaux – Sud-Ouest, Talence, Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, SISTM Team, UMR 1219, F-33000 Bordeaux, France
Vignals Carole: Inria, Inria Bordeaux – Sud-Ouest, Talence, Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, SISTM Team, UMR 1219, F-33000 Bordeaux, France
Lehot Laurent: Inria, Inria Bordeaux – Sud-Ouest, Talence, Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, SISTM Team, UMR 1219, F-33000 Bordeaux, France
Thiébaut Rodolphe: Inria, Inria Bordeaux – Sud-Ouest, Talence, Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, SISTM Team, UMR 1219, F-33000 Bordeaux, France
Moireau Philippe: ISPED Inserm U1219 Bordeaux Population Health Bureau 23 146 rue Leo Saignat CS 61292 33076 Bordeaux Cedex, France
Prague Mélanie: Inria, Inria Saclay-Ile de France, France and LMS, CNRS UMR 7649, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France

The International Journal of Biostatistics, 2024, vol. 20, issue 1, 13-41

Abstract: In response to the COVID-19 pandemic caused by SARS-CoV-2, governments have adopted a wide range of non-pharmaceutical interventions (NPI). These include stringent measures such as strict lockdowns, closing schools, bars and restaurants, curfews, and barrier gestures such as mask-wearing and social distancing. Deciphering the effectiveness of each NPI is critical to responding to future waves and outbreaks. To this end, we first develop a dynamic model of the French COVID-19 epidemics over a one-year period. We rely on a global extended Susceptible-Infectious-Recovered (SIR) mechanistic model of infection that includes a dynamic transmission rate over time. Multilevel data across French regions are integrated using random effects on the parameters of the mechanistic model, boosting statistical power by multiplying integrated observation series. We estimate the parameters using a new population-based statistical approach based on a Kalman filter, used for the first time in analysing real-world data. We then fit the estimated time-varying transmission rate using a regression model that depends on the NPIs while accounting for vaccination coverage, the occurrence of variants of concern (VoC), and seasonal weather conditions. We show that all NPIs considered have an independent significant association with transmission rates. In addition, we show a strong association between weather conditions that reduces transmission in summer, and we also estimate increased transmissibility of VoC.

Keywords: COVID-19; epidemic modeling; Kalman filters; non-pharmaceutical interventions; population estimation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/ijb-2022-0087

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